Integer types (int8, int16, int32, int64) in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does int8 represent in numpy?

int8 is an integer type that uses 8 bits to store values. It can hold numbers from -128 to 127.

beginner

How many bytes does int16 use and what is its value range?

int16 uses 2 bytes (16 bits) and can store integers from -32,768 to 32,767.

intermediate

Why choose int32 over int8 in numpy arrays?

int32 can store much larger numbers (from about -2 billion to 2 billion) than int8. Use it when you need bigger integer values.

intermediate

What is the difference between int64 and int32?

int64 uses 8 bytes and can store much larger integers (from about -9 quintillion to 9 quintillion) compared to int32 which uses 4 bytes.

intermediate

How does choosing the right integer type affect memory usage in numpy?

Choosing smaller integer types like int8 saves memory but limits the range of values. Larger types like int64 use more memory but can store bigger numbers.

Which numpy integer type uses 1 byte of memory?

A int64

B int16

C int8

D int32

int8 uses 1 byte (8 bits) of memory.

What is the maximum value that int16 can store?

A 32,767

B 127

C 2,147,483,647

D 9,223,372,036,854,775,807

int16 stores values from -32,768 to 32,767.

If you need to store very large integers, which numpy type should you use?

A int8

B int64

C int32

D int16

int64 can store the largest integer values among these options.

Which integer type uses 4 bytes of memory?

A int32

B int16

C int8

D int64

int32 uses 4 bytes (32 bits) of memory.

What happens if you store a number larger than the max value of an integer type in numpy?

A It converts to float automatically

B It raises an error

C It stores the number correctly

D It wraps around or causes overflow

Storing a number larger than the max value causes overflow, wrapping around to negative or unexpected values.

Explain the differences between numpy integer types int8, int16, int32, and int64 in terms of memory size and value range.

Think about how many bits each type uses and how that affects the numbers they can store.

Describe why choosing the correct integer type is important when working with numpy arrays.

Consider what happens if the type is too small or unnecessarily large.

Practice

1. Which numpy integer type uses the least amount of memory?

easy

A. int64

B. int16

C. int8

D. int32

Solution

1. Step 1: Understand integer type sizes

   Each numpy integer type uses a different number of bits: int8 uses 8 bits, int16 uses 16 bits, int32 uses 32 bits, and int64 uses 64 bits.

2. Step 2: Identify the smallest size

   Since 8 bits is the smallest size, int8 uses the least memory.

3. Final Answer:

   int8 -> Option C

4. Quick Check:

   Smallest bits = int8 [OK]

Hint: Smaller int types use fewer bits and less memory [OK]

Common Mistakes:

• Confusing int8 with int16 or larger types
• Thinking int64 uses less memory
• Assuming int32 is smallest

2. Which of the following is the correct way to create a numpy array of type int16?

easy

A. np.array([1, 2, 3], dtype='int16')

B. np.array([1, 2, 3], type='int16')

C. np.array([1, 2, 3], dtype=int8)

D. np.array([1, 2, 3], type=int16)

Solution

1. Step 1: Recall numpy array creation syntax

   To specify the data type, use the parameter dtype with a string or numpy type.

2. Step 2: Check each option

   np.array([1, 2, 3], dtype='int16') uses dtype='int16', which is correct. Options B and D use type which is invalid. np.array([1, 2, 3], dtype=int8) uses dtype=int8, which is valid syntax but wrong type.

3. Final Answer:

   np.array([1, 2, 3], dtype='int16') -> Option A

4. Quick Check:

   Use dtype='int16' to set type [OK]

Hint: Use dtype='intXX' to set integer type in numpy array [OK]

Common Mistakes:

• Using 'type' instead of 'dtype'
• Passing dtype without quotes for string type
• Confusing int8 and int16 types

3. What will be the output of this code?

import numpy as np
arr = np.array([127, 128], dtype='int8')
print(arr)

medium

A. [127 128]

B. [127 -128]

C. [127 0]

D. Error: value out of range

Solution

1. Step 1: Understand int8 range

   int8 can store values from -128 to 127. The value 128 is outside this range.

2. Step 2: Observe overflow behavior

   When 128 is stored in int8, it wraps around to -128 due to overflow.

3. Final Answer:

   [127 -128] -> Option B

4. Quick Check:

   int8 overflow wraps 128 to -128 [OK]

Hint: int8 max is 127; values above wrap to negative [OK]

Common Mistakes:

• Expecting 128 to store correctly in int8
• Assuming error raised on overflow
• Thinking 128 becomes 0

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([1000, 2000], dtype='int8')
print(arr)

medium

A. No error, prints [1000 2000]

B. SyntaxError due to dtype

C. ValueError: integer out of range

D. Values overflow and wrap around silently

Solution

1. Step 1: Check dtype and values

   The array uses int8 which can only hold -128 to 127, but values 1000 and 2000 are much larger.

2. Step 2: Understand numpy overflow behavior

   Instead of error, numpy silently wraps values using modulo 256, causing overflow.

3. Final Answer:

   Values overflow and wrap around silently -> Option D

4. Quick Check:

   int8 overflow wraps large values silently [OK]

Hint: Large values in small int types wrap silently, no error [OK]

Common Mistakes:

• Expecting ValueError on overflow
• Thinking values print as is
• Confusing syntax error with dtype

5. You have a large dataset of whole numbers ranging from -30000 to 30000. Which numpy integer type should you choose to store this data efficiently without overflow?

hard

A. int16

B. int32

C. int8

D. int64

Solution

1. Step 1: Identify the value range needed

   The data ranges from -30000 to 30000, so the integer type must cover at least this range.

2. Step 2: Check integer type ranges

   int8 covers -128 to 127 (too small), int16 covers -32768 to 32767 (fits range), int32 and int64 cover larger ranges but use more memory.

3. Step 3: Choose the smallest type that fits

   int16 fits the range and uses less memory than int32 or int64.

4. Final Answer:

   int16 -> Option A

5. Quick Check:

   int16 range covers ±30000 efficiently [OK]

Hint: Pick smallest int type covering data range to save memory [OK]

Common Mistakes:

• Choosing int8 which is too small
• Picking int64 unnecessarily
• Ignoring negative range limits